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dcgan-tensorflow's Introduction

DCGAN in Tensorflow

Tensorflow implementation of Deep Convolutional Generative Adversarial Networks which is a stabilize Generative Adversarial Networks. The referenced torch code can be found here.

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  • Brandon Amos wrote an excellent blog post and image completion code based on this repo.
  • To avoid the fast convergence of D (discriminator) network, G (generator) network is updated twice for each D network update, which differs from original paper.

Online Demo




First, download dataset with:

$ python mnist celebA

To train a model with downloaded dataset:

$ python --dataset mnist --input_height=28 --output_height=28 --train
$ python --dataset celebA --input_height=108 --train --crop

To test with an existing model:

$ python --dataset mnist --input_height=28 --output_height=28
$ python --dataset celebA --input_height=108 --crop

Or, you can use your own dataset (without central crop) by:

$ mkdir data/DATASET_NAME
... add images to data/DATASET_NAME ...
$ python --dataset DATASET_NAME --train
$ python --dataset DATASET_NAME
$ # example
$ python --dataset=eyes --input_fname_pattern="*_cropped.png" --train




After 6th epoch:


After 10th epoch:


Asian face dataset





MNIST codes are written by @PhoenixDai.




More results can be found here and here.

Training details

Details of the loss of Discriminator and Generator (with custom dataset not celebA).



Details of the histogram of true and fake result of discriminator (with custom dataset not celebA).




Taehoon Kim / @carpedm20

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